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 Results 1 - 20 of 37  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-18
Online Online Unsupervised Training of Sequential Neural Beamformer Using Blindly-separated and Non-separated Signals
Kohei Saijo, Tetsuji Ogawa (Waseda Univ.)
(To be available after the conference date) [more]
EA, SIP, SP, IPSJ-SLP [detail] 2022-03-01
(Primary: On-site, Secondary: Online)
Target speaker extraction based on conditional variational autoencoder and directional information in underdetermined condition
Rui Wang, Li Li, Tomoki Toda (Nagoya Univ)
This paper deals with a dual-channel target speaker extraction problem in underdetermined conditions. A blind source sep... [more] EA2021-76 SIP2021-103 SP2021-61
EA, US, SP, SIP, IPSJ-SLP [detail] 2021-03-03
Online Online [Invited Talk] *
Masahito Togami (LINE) EA2020-64 SIP2020-95 SP2020-29
Recently, deep learning based speech source separation has been evolved rapidly. A neural network (NN) is usually learne... [more] EA2020-64 SIP2020-95 SP2020-29
EA, SIP, SP 2019-03-14
Nagasaki i+Land nagasaki (Nagasaki-shi) Blind speech separation based on approximate joint diagonalization utilizing correlation between neighboring frequency bins
Taiki Asamizu, Toshihiro Furukawa (TUS) EA2018-100 SIP2018-106 SP2018-62
In this paper, we propose a new method that extends the approximate joint diagonalization blind speech separation (BSS).... [more] EA2018-100 SIP2018-106 SP2018-62
EA, SIP, SP 2019-03-15
Nagasaki i+Land nagasaki (Nagasaki-shi) [Poster Presentation] Design and Evaluation of Ladder Denoising Autoencoder for Auditory Speech Feature Extraction of Overlapped Speech Separation
Hiroshi Sekiguchi, Yoshiaki Narusue, Hiroyuki Morikawa (Univ. of Tokyo) EA2018-155 SIP2018-161 SP2018-117
Primates and mammalian distinguish overlapped speech sounds from one another by recognizing a single sound source whethe... [more] EA2018-155 SIP2018-161 SP2018-117
EA, ASJ-H 2018-08-23
Miyagi Tohoku Gakuin Univ. Self-produced speech enhancement and suppression method with wearable air- and body-conductive microphones
Moe Takada, Shogo Seki, Tomoki Toda (Nagoya Univ.) EA2018-29
This paper presents a self-produced speech enhancement and suppression method for multichannel signals recorded with bot... [more] EA2018-29
(Joint) [detail]
Okinawa   Stable Estimation Method of Spatial Correlation Matrices for Multi-channel NMF
Yuuki Tachioka (Denso IT Lab) EA2017-103 SIP2017-112 SP2017-86
Multi-channel non-negative matrix factorization (MNMF) achieves a high sound source separation performance but its initi... [more] EA2017-103 SIP2017-112 SP2017-86
EA 2018-02-16
Hiroshima Pref. Univ. Hiroshima The effect of increasing the number of channels with multi-channel non-negative matrix factorization for noisy speech recognition
Takanobu Uramoto (Oita Univ.), Youhei Okato, Toshiyuki Hanazawa (Mitsubishi Electric), Iori Miura, Shingo Uenohara, Ken'ich Furuya (Oita Univ.) EA2017-99
Nonnegative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative matrices. In the field of ... [more] EA2017-99
(Joint) [detail]
Tokyo Waseda Univ. Green Computing Systems Research Organization A Sound Source Separation Method for Multiple Person Speech Recognition using Wavelet Analysis Based on Sound Source Position Obtained by Depth Sensor
Nobuhiro Uehara, Kazuo Ikeshiro, Hiroki Imamura (Soka Univ.) SP2017-63
Recently, voice information guidance systems are used for only one person in operating at a city hall. To realize operat... [more] SP2017-63
WIT, SP 2017-10-19
Fukuoka Tobata Library of Kyutech (Kitakyushu) Speech enhancement of utterance while playing with werewolf game "JINRO" based on NMF
Shunsuke Kawano, Toru Takahashi (OSU) SP2017-35 WIT2017-31
We describe that speech enhancement for natural and multi speaker dialognue. To record natural and multi speaker dialogn... [more] SP2017-35 WIT2017-31
SP 2017-08-30
Kyoto Kyoto Univ. [Poster Presentation] Semi-blind speech separation and enhancement using recurrent neural network
Masaya Wake, Yoshiaki Bando, Masato Mimura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara (Kyoto Univ.) SP2017-22
This paper describes a semi-blind speech enhancement method using a neural network.
In a human-robot speech interaction... [more]
CAS, ICTSSL 2017-01-26
Tokyo Kikai-Shinko-Kaikan Bldg. Target Sound Enhancement by Post Processing of Sound Source Separation
Naoki Shinohara, Kenji Suyama (Tokyo Denki Univ.) CAS2016-77 ICTSSL2016-31
Although several methods have been proposed for sound source separation, a suppression ability of interference sound is ... [more] CAS2016-77 ICTSSL2016-31
EA, EMM 2015-11-12
Kumamoto Kumamoto Univ. Noise suppression method for body-conducted soft speech based on external noise monitoring
Yusuke Tajiri (NAIST), Tomoki Toda (Nagoya Univ.), Satoshi Nakamura (NAIST) EA2015-31 EMM2015-52
As one of the silent speech interfaces, nonaudible murmur (NAM) microphone has been developed for detecting an extremely... [more] EA2015-31 EMM2015-52
EA 2014-10-24
Tokyo Central Research Laboratory, Hitachi, Ltd. [Invited Talk] Speech enhancement techniques in multi-speaker spontaneous speech recognition for conversation scene analysis
Shoko Araki, Takaaki Hori, Tomohiro Nakatani (NTT) EA2014-25
This paper illustrates speech enhancement techniques for multi-speaker distant-talk speech recognition, where a conversa... [more] EA2014-25
SIS 2013-12-12
Tottori Torigin Bunka Kaikan (Tottori) [Tutorial Lecture] Enhancement and Separation for Speech Signals
Arata Kawamura (Osaka Univ.) SIS2013-35
In this paper, we discus about three main topics of speech processing technologies. First, we review and discuss about a... [more] SIS2013-35
SP, IPSJ-SLP 2012-12-21
Tokyo TITECH(Ookayama) Reduction of cross spectrum for feature-domain sound source separation
Atsushi Ando (Nagoya Univ.), Kenta Niwa (NTT), Norihide Kitaoka, Kazuya Takeda (Nagoya Univ.) SP2012-93
Speech source separation is utilized for recognition of simultaneous speech. Conventional source separation methods, esp... [more] SP2012-93
EA, EMM 2012-11-16
Oita OITA Univ. Auxiliary-function-based independent vector analysis with non-speech frame information for speech enhancement
Masataka Suzuki (Univ. of Tokyo), Nobutaka Ono (NII), Toru Taniguchi, Masaru Sakai, Akinori Kawamura (Toshiba Corp.), Miquel Espi, Shigeki Sagayama (Univ. of Tokyo) EA2012-87 EMM2012-69
In this study, we discuss a technique to enhance the speech of interest in the noisy environment with using microphone a... [more] EA2012-87 EMM2012-69
PRMU, SP 2012-02-10
Miyagi   Multi-band speech recognition using confidence of blind source separation
Atsushi Ando, Hiromasa Ohashi (Nagoya Univ.), Sunao Hara (NAIST), Norihide Kitaoka, Kazuya Takeda (Nagoya Univ.) PRMU2011-234 SP2011-149
One of the main applications of Blind Source Separation (BSS) is to improve performance of Automatic Speech Recognition ... [more] PRMU2011-234 SP2011-149
EA 2011-03-18
Aichi Nagoya Univ. Tiny-setup Blind Source Separation via Time-Varying Softmask based on Alternative Separation Matrix
Kazunobu Kondo, Yu Takahashi, Seiichi Hashimoto (Yamaha Corp.), Takanori Nishino (Mie Univ.), Kazuya Takeda (Nagoya Univ.) EA2010-126
Frequency domain independent component analyis has received much attention from many industries for high performace spee... [more] EA2010-126
SP 2011-01-27
Kyoto NICT [Invited Talk] Robot Audition -- Hands-Free Automatic Speech Recognition under Highly-Noisy Environemnts --
Kazuhiro Nakadai (HRI-JP/Tokyo Tech.), Hiroshi G. Okuno (Kyoto Univ.) SP2010-104
This paper addresses robot audition, which realizes listening capabilities for robots using robot-embedded microphones. ... [more] SP2010-104
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